Toxosources Consumption Survey
Arno Swart
7/19/2022
Here we present graphs of the results of the Toxosources consumer survey. Raw data files are available at…
library( RColorBrewer )
library( tidyverse )
library( haven )
library( here )
setwd(here("consumption", "GFK survey"))
source( "functions.R" )
df_survey_info <- read_survey_info()
df_survey_freq <- read_survey_freq()
df_survey_process <- read_survey_process()
df_survey_type <- read_survey_type()
my_theme <- theme( panel.background = element_blank(),
panel.grid.major = element_blank(),
strip.text = element_text( size=15 ),
axis.title = element_text( size=15 ),
axis.text = element_text( size=15 ),
plot.title = element_text( size=25 ),
legend.title = element_text( size=15 ),
legend.text = element_text( size=15 ),
plot.margin = margin(0, 1, 0, 0, "cm"),
legend.position="bottom" )1 Consumption Frequencies
1.1 Meat in general
This category includes all meat, including meat which is heated thoroughly and therefore microbiologically safe.
df_survey_process %>%
dplyr::select( freq_meat_general, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_meat_general ) %>%
plot_freq( "Consumption frequency of meat", freq_meat_general )
#ggsave("figures/freq_meat_general.png", width = 35, height = 20, units="cm", dpi=300 )1.2 Undercooked meat consumption frequency
freq_meat_labels = c(
"freq_meat_beef_veal"="Veal",
"freq_meat_pork"="Pork",
"freq_meat_poultry"="Poultry",
"freq_meat_lamb"="Lamb",
"freq_meat_mutton"="Mutton",
"freq_meat_horse"="Horse",
"freq_meat_other"="Other",
"freq_meat_game"="Game",
"freq_meat_wild_birds"="Birds",
"freq_meat_mince_beef"="Beef",
"freq_meat_mince_pork"="Pork",
"freq_meat_mince_mix"="Mix",
"freq_meat_mince_misc"="Misc",
"freq_meat_sausage"="Sausage",
"freq_meat_spread"="Spread",
"freq_meat_cured_pork"="Cured Pork",
"freq_meat_cured_beef"="Cured Beef",
"freq_meat_cured_misc"="Cured Misc",
"freq_meat_dry_fermented"="Dry Fermented",
"freq_meat_dried"= "Dried Meat",
"freq_meat_carpaccio"="Carpaccio",
"freq_meat_tartare"= "Tartare" )
df_survey_freq %>%
dplyr::select( starts_with("freq_meat"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "freq_meat"), names_to = "meat", values_to = "consumed" ) %>%
calc_perc( meat, consumed ) %>%
plot_freq( "Frequency of consumption of meat products",
fill_by=consumed, wrap_by=meat, my_labels=freq_meat_labels )+
guides(fill = guide_legend(nrow = 3, byrow = T))
#ggsave("figures/freq_meat.png", width = 35, height = 25, units="cm", dpi=300)1.3 Raw meat consumption frequency
freq_labels_raw= c( "freq_raw_shell" = "Shellfish",
"freq_raw_meatball" = "Meatball",
"freq_raw_milk" = "Raw Milk" )
df_survey_freq %>% #
dplyr::select( starts_with("freq_raw"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "freq_raw"), names_to = "raw", values_to = "consumed" ) %>%
calc_perc( raw, consumed ) %>%
plot_freq( "Frequency of consumption of raw products",
fill_by=consumed, wrap_by=raw, my_labels=freq_labels_raw )+
guides(fill = guide_legend(nrow = 3, byrow = T))
#ggsave("figures/freq_raw.png", width = 35, height = 25, units="cm", dpi=300)1.4 Fruit and Vegetables Consumption Frequencies
freq_veg_labels = c("freq_veg_berries" = "Berries",
"freq_veg_pome_fr" = "Pomme Fruit",
"freq_veg_brassic" = "Brassica",
"freq_veg_cucurbi" = "Cucurbi",
"freq_veg_tomato" = "Tomato",
"freq_veg_peppers" = "Peppers",
"freq_veg_fungi" = "Fungi",
"freq_veg_herbs" = "Herbs",
"freq_veg_leafy_veg" = "Leafy Vegetables",
"freq_veg_sprouts" = "Sprouts",
"freq_veg_leafy_misc" = "Leafy Misc",
"freq_veg_roots" = "Roots",
"freq_veg_stems" = "Stems",
"freq_veg_fermented" = "Fermented",
"freq_veg_asparagus" = "Asperagus",
"freq_veg_sugarsnaps" = "Sugarsnaps",
"freq_veg_ginger" = "Ginger",
"freq_veg_salicornia" = "Salicornia" )
df_survey_freq %>%
dplyr::select( starts_with("freq_veg"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "freq_veg"), names_to = "veg", values_to = "consumed" ) %>%
calc_perc( veg, consumed ) %>%
plot_freq( "Frequency of consumption of vegetables",
fill_by=consumed, wrap_by=veg, my_labels=freq_veg_labels )
#ggsave("figures/freq_veg.png", width = 15, height = 15)1.5 Country specialties
1.5.1 Czech Republic
cz_labels= c( "freq_cz_tatarak_divocaka" = "Tatarak divocaka",
"freq_cz_pecene_veprove" = "Pecene veprove",
"freq_cz_veprovy_mozecek" = "Veprovy mozecek",
"freq_cz_jitrnice" = "Jitrnice",
"freq_cz_jelito" = "Jelito",
"freq_cz_tlacenka" = "Tlacenka" )
df_survey_freq %>%
dplyr::select( starts_with("freq_cz"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Czech Republic" ) %>%
pivot_longer( cols=starts_with( "freq_cz"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Czech Republic specialty products",
fill_by=consumed, my_labels=cz_labels )
#ggsave("figures/freq_cz_specialty.png", width = 35, height = 25, units="cm", dpi=300)1.5.2 Germany
de_labels= c( "freq_de_hackepeter" = "hackepeter",
"freq_de_knackwurst" = "knackwurst" )
df_survey_freq %>%
dplyr::select( starts_with("freq_de"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Germany" ) %>%
pivot_longer( cols=starts_with( "freq_de"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of German specialty products",
fill_by=consumed, my_labels=de_labels )
#ggsave("figures/freq_de_specialty.png", width = 35, height = 25, units="cm", dpi=300)1.5.3 Denmark
dk_labels <- c( "freq_dk_medisterpolse" = "Medisterpolse",
"freq_dk_rullepolse" = "Rullepolse",
"freq_dk_hjerter_flodesovs" = "Hjerter flodesovs" )
df_survey_freq %>%
dplyr::select( starts_with("freq_dk"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Denmark" ) %>%
pivot_longer( cols=starts_with( "freq_dk"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Danish specialty products",
fill_by=consumed, my_labels=dk_labels )
#ggsave("figures/freq_dk_specialty.png", width = 35, height = 25, units="cm", dpi=300)1.5.4 France
fr_labels <- c( "freq_fr_figatelle" = "Figatelle",
"freq_fr_mettwurst" = "Mettwurst",
"freq_fr_museau_porc" = "Museau Porc",
"freq_fr_museau_boeuf" = "Museau Boeuf" )
df_survey_freq %>%
dplyr::select( starts_with("freq_fr"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "France" ) %>%
pivot_longer( cols=starts_with( "freq_fr"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of French specialty products",
fill_by=consumed, my_labels=fr_labels )
#ggsave("figures/freq_fr_specialty.png", width = 35, height = 25, units="cm", dpi=300)1.5.5 Netherlands
nl_labels <- c( "freq_nl_filet_americain" = "Filet Americain",
"freq_nl_paardenrookvlees" = "Paardenrookvlees",
"freq_nl_ossenworst" = "Ossenworst",
"freq_nl_theeworst" = "Theeworst",
"freq_nl_rosbief" = "Rosbief" )
df_survey_freq %>%
dplyr::select( starts_with("freq_nl"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "The Netherlands" ) %>%
pivot_longer( cols=starts_with( "freq_nl"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Dutch specialty products",
fill_by=consumed, my_labels=nl_labels )
#ggsave("figures/freq_nl_specialty.png", width = 35, height = 25, units="cm", dpi=300)1.5.6 Norway
no_labels <- c( "freq_no_lammerull" = "Lammerull",
"freq_no_sylterull" = "Sylterull",
"freq_no_kjottrull" = "Kjottrull",
"freq_no_smalahove" = "Smalahove",
"freq_no_whale" = "Whale" )
df_survey_freq %>%
dplyr::select( starts_with("freq_no"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Norway" ) %>%
pivot_longer( cols=starts_with( "freq_no"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Norwegian specialty products",
fill_by=consumed, my_labels=no_labels )
#ggsave("figures/freq_no_specialty.png", width = 35, height = 25, units="cm", dpi=300)1.5.7 Spain
es_labels <- c( "freq_es_morcilla" = "Morcilla",
"freq_es_lomo_orza" = "Lomo Orza" )
df_survey_freq %>%
dplyr::select( starts_with("freq_es"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Spain" ) %>%
pivot_longer( cols=starts_with( "freq_es"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Spanish specialty products",
fill_by=consumed, my_labels=es_labels )
#ggsave("figures/freq_es_specialty.png", width = 35, height = 25, units="cm", dpi=300)2 Types of meat consumed
2.1 Poultry
labels_poultry = c( "type_poultry_chicken" = "Chicken",
"type_poultry_turkey" = "Turkey",
"type_poultry_duck" = "Duck",
"type_poultry_goose" = "Goose",
"type_poultry_ostrich" = "Ostrich",
"type_poultry_misc" = "Misc" )
df_survey_type %>%
dplyr::select( starts_with("type_poultry"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "poultry", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( poultry, consumed ) %>%
plot_freq( "Type of poultry consumed", fill_by=consumed,
wrap_by=poultry, my_labels=labels_poultry )
#ggsave("figures/type_poultry.png", width=20, height=20, units="cm" )2.2 Lamb and Goat
labels_goat <- c(
"type_sheep_lamb" = "Sheep lamb",
"type_sheep_adult" = "Sheep adult",
"type_goat_lamb" = "Goat lamb",
"type_goat_adult" = "Goat adult" )
df_survey_type %>%
dplyr::select( starts_with("type_sheep"), starts_with("type_goat"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "livestock", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( livestock, consumed ) %>%
plot_freq( "Type of goat or sheep consumed", fill_by=consumed,
wrap_by=livestock, my_labels=labels_goat )
#ggsave("figures/type_sheep_goat.png", width=20, height=20, units="cm" )2.3 Other Livestock
labels_livestock <- c(
"type_livestock_donkey" = "Donkey",
"type_livestock_buffalo" = "Buffalo",
"type_livestock_rabbit" = "Rabbit",
"type_livestock_reindeer" = "Reindeer",
"type_livestock_misc" = "Misc" )
df_survey_type %>%
dplyr::select( starts_with("type_livestock"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "livestock", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( livestock, consumed ) %>%
plot_freq( "Type of livestock consumed", fill_by=consumed,
wrap_by=livestock, my_labels=labels_livestock )
#ggsave("figures/type_livestock.png", width=30, height=20, units="cm" )2.4 Game
labels_game <- c(
"type_game_boar" = "Boar",
"type_game_rabbit" = "Rabbit",
"type_game_hare" = "Hare",
"type_game_reindeer" = "Reindeer",
"type_game_moose" = "Moose",
"type_game_misc_deer" = "Misc. Deer",
"type_game_mouflon" = "Mouflon",
"type_game_chamois" = "Chamois",
"type_game_ibex" = "Ibex",
"type_game_misc" = "Misc." )
df_survey_type %>%
dplyr::select( starts_with("type_game"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "game", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( game, consumed ) %>%
plot_freq( "Type of game consumed", fill_by=consumed,
wrap_by=game, my_labels=labels_game )
#ggsave("figures/type_game.png", width=30, height=20, units="cm" )2.5 Wild Birds
labels_wildbird <- c(
"type_wild_bird_duck" = "Duck",
"type_wild_bird_goose" = "Goose",
"type_wild_bird_pheasant" = "Pheasant",
"type_wild_bird_quail" = "Quail",
"type_wild_bird_partridge" = "Partridge",
"type_wild_bird_grouse" = "Grouse",
"type_wild_bird_guineafowl" = "Guineafowl",
"type_wild_bird_pigeon" = "Pigeon",
"type_wild_bird_ptarmigan" = "Ptarmigan",
"type_wild_bird_misc" = "Misc." )
df_survey_type %>%
dplyr::select( starts_with("type_wild"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "wildbird", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( wildbird, consumed ) %>%
plot_freq( "Type of wild bird consumed", fill_by=consumed,
wrap_by=wildbird, my_labels=labels_wildbird )
#ggsave("figures/type_wild_birds.png", width=20, height=20, units="cm" )3 Purchase and Storage Preferences
3.1 Frequency of buying RTE fruit and vegetables
df_survey_process %>%
dplyr::select( freq_veg_sold_ready, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_veg_sold_ready ) %>%
plot_freq( "Frequency of consuming vegetable and fruit RTE", freq_veg_sold_ready )
#ggsave("figures/freq_veg_ready.png", width=30, height=15, units="cm", dpi=300 )3.2 Frequency of buying fruit and vegetables from a small scale producer
df_survey_process %>%
dplyr::select( freq_veg_sold_small_scale, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_veg_sold_small_scale ) %>%
plot_freq("Frequency of vegetables bought from small scale producer", freq_veg_sold_small_scale )
#ggsave("figures/freq_veg_small.png", width=30, height=20, units="cm")3.3 Frequency of buying organic meat
df_survey_process %>%
dplyr::select( freq_meat_organic, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_meat_organic ) %>%
plot_freq( "Frequency of buying organic meat", freq_meat_organic )
#ggsave("figures/freq_meat_organic.png", width=35, height = 15, units="cm")3.4 Frequency of buying meat frozen
df_survey_process %>%
dplyr::select( freq_meat_frozen, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_meat_frozen ) %>%
plot_freq( "Frequency of buying meat frozen", freq_meat_frozen )
#ggsave("figures/freq_meat_frozen.png", width=20, height = 15, units="cm")3.5 Vegetable storage location
store_labels <- c(
"store_veg_berries" = "Berries",
"store_veg_herbs" = "Herbs",
"store_veg_leaf" = "Leafy Greens",
"store_veg_fermented" = "Fermented vegetables" )
df_survey_process %>%
dplyr::select( starts_with("store_veg"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "store_"), names_to = "prep", values_to = "stored" ) %>%
calc_perc( prep, stored ) %>%
plot_freq( "Frequency of storing vegetable in fridge or freezer",
fill_by=stored, wrap_by=prep, my_labels=store_labels )
#ggsave("figures/store_veg.png", width = 30, height = 20, units="cm" )3.6 Meat storage location
store_meat_labels <- c(
"store_meat_cut" = "Meat Cuts",
"store_meat_mince" = "Minced Meat",
"store_meat_sausage_fresh" = "Fresh Sausage",
"store_meat_spread" = "Meat Spread",
"store_meat_cured" = "Cured Meat",
"store_meat_dry_fermented" = "Dry Fermented Sausage",
"store_meat_dry" = "Dry Sausage"
)
df_survey_process %>%
dplyr::select( starts_with("store_meat"), id ) %>%
left_join( df_survey_info, by="id") %>%
pivot_longer( cols=starts_with( "store_"), names_to = "prep", values_to = "stored" ) %>%
calc_perc( prep, stored ) %>%
plot_freq( my_title="Frequency of storing meat in fridge or freezer",
fill_by=stored, wrap_by=prep, my_labels=store_meat_labels ) +
guides(fill = guide_legend(nrow = 3, byrow = T))
#ggsave("figures/store_meat.png", height = 25, width = 37, units="cm", dpi=300 )3.7 Quality of your freezer
df_survey_process %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( quality_storage_meat ) %>%
plot_freq( "Quality of the freezer",
fill_by=quality_storage_meat, wrap_by=NULL, my_labels=NULL ) +
scale_fill_manual("legend", values = c(brewer.pal(3, "Blues"),"brown","dark grey","light grey"))+
guides( "", fill = guide_legend(title="", nrow = 3, byrow = T))## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
#ggsave("figures/process_quality_freezer.png", width = 35, height = 25, units="cm", dpi=300 )4 Preparation Preferences
4.1 Frequency of washing vegetables
wash_labels= c(
"wash_veg_ready" = "Ready to eat",
"wash_veg_berries" = "Berries",
"wash_veg_pome_fr" = "Pomme Fruit",
"wash_veg_brassic" = "Brassica",
"wash_veg_cucurbi" = "Cucurbi",
"wash_veg_tomato" = "Tomato",
"wash_veg_peppers" = "Peppers",
"wash_veg_fungi" = "Fungi",
"wash_veg_herbs" = "Herbs",
"wash_veg_leafy_veg" = "Leafy vegetables",
"wash_veg_sprouts" = "Sprouts",
"wash_veg_leafy_misc" = "Misc",
"wash_veg_roots" = "Roots",
"wash_veg_stems" = "Stems" )
df_survey_process %>%
dplyr::select( starts_with("wash_veg"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "wash_"), names_to = "prep", values_to = "washed" ) %>%
calc_perc( prep, washed ) %>%
plot_freq( "Frequency of washing vegetables",
wrap_by=prep, fill_by=washed, my_labels=wash_labels )
#ggsave("figures/freq_wash_veg.png", width = 35, height = 25, units="cm" )5 Consumption Preferences
5.1 Preparation Rare/Medium/Welldone
prep_labels = c( "prep_meat_cuts" = "Meat Cuts",
"prep_meat_mince" = "Minced Meat",
"prep_meat_sausage" = "Sausage" )
df_survey_process %>%
dplyr::select( starts_with("prep_meat"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "prep_"), names_to = "prep", values_to = "cooked" ) %>%
calc_perc( prep, cooked ) %>%
plot_freq( "Frequency of preparation style", cooked,
wrap_by=prep, my_labels=prep_labels )
#ggsave("figures/prep_style_meat.png", width = 25, height = 15, units="cm", dpi=300 )5.2 Frequency of eating fruit and vegetables raw
labels_freq_raw_veg <- c(
"freq_fruit_raw_unpeeled" = "Raw unpeeled fruit",
"freq_veg_raw_smoothie" = "Raw vegetable smoothie",
"freq_veg_raw" = "Raw vegetables" )
df_survey_process %>%
dplyr::select( freq_fruit_raw_unpeeled, freq_veg_raw_smoothie, freq_veg_raw, id ) %>%
pivot_longer( -id, names_to="fruit", values_to="freq" ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc(fruit, freq ) %>%
plot_freq("Frequency of eating fruit or vegetables raw", freq,
wrap_by=fruit, my_labels=labels_freq_raw_veg )
#ggsave("figures/freq_raw_fruit_veg.png", width = 35, height = 25, units="cm", dpi=300 )5.3 Fermented sausage type prefered
df_survey_type %>%
dplyr::select( type_fermented_sausage , id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter(!is.na(type_fermented_sausage)) %>%
calc_perc( type_fermented_sausage ) %>%
plot_freq( "Type of fermented sausage preferred", type_fermented_sausage )
#ggsave("figures/type_fermented_sausage.png", width=40, height=20, units="cm" )5.4 Portion Sizes
5.4.1 Meatball portion size
TODO: insert figures
df_survey_process %>%
dplyr::select( vol_meat_meatball, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( vol_meat_meatball ) %>%
plot_freq( "Volume of meatball consumed", vol_meat_meatball )
#ggsave("figures/vol_meatball.png", width=30, height = 15, units="cm")5.4.2 Sausage portionsize
TODO: insert figures
df_survey_process %>%
dplyr::select( vol_meat_sausage, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( vol_meat_sausage ) %>%
plot_freq( "Volume of sausage consumed", vol_meat_sausage )
#ggsave("figures/vol_sausage.png", width=30, height = 15, units="cm")5.4.3 Meat cut portion size
TODO: insert figures
df_survey_process %>%
dplyr::select( vol_meat_cut, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( vol_meat_cut ) %>%
plot_freq( "Volume of meat cuts consumed", vol_meat_cut )
ggsave("figures/vol_meatcut.png", width=30, height = 15, units="cm")5.4.4 Raw vegetables portion sizes
Note, something was clearly wrong here. Respondents were asked to supply the number of 55 gram portions, but probably supplied the total weight. We have filtered the figure to only show 550g total or less. TODO: insert figures
df_survey_process %>%
left_join( df_survey_info, by="id" ) %>%
filter( vol_veg_raw < 10 ) %>%
mutate( vol_veg_raw = vol_veg_raw * 55 ) %>%
ggplot( ) +
geom_histogram( aes(vol_veg_raw, y=..density.., fill=country),
position="dodge", binwidth=20) +
geom_density( aes(vol_veg_raw, color=country), bw=30 ) +
scale_x_continuous( "Weight [g]" ) +
ggtitle( "Portion size raw vegetables" ) +
my_theme## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
#ggsave("figures/vol_raw_veg.png", width=25, height = 15, units="cm")6 Miscelaneous
6.1 Volume of raw meat sampled
When preparing e.g. steak tartare some people may sample the meat for taste.
# TODO: check NA meaning
df_survey_process %>%
dplyr::select( starts_with("vol_meat_raw"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( vol_meat_raw_sample ) %>%
plot_freq( "Amount of raw meat sampled", vol_meat_raw_sample )
ggsave("figures/raw_meat_sampled.png", width = 20, height = 15, units="cm")